Postgraduate Virtual Open Day 18 July 2018
12 -3pm, Find out more
Places on this programme are limited. We will give equal consideration to all applications received by 1 August 2018. We will still accept applications received after this date, but may not be able to offer a place if the programme is full.
This programme aims at training those who would like to pursue a career in the booming field of Artificial Intelligence (AI). It uniquely covers all five of the hottest AI topics – games, robotics, vision, music, and language – each backed up by a respective research group at QMUL that is world-leading. Practical machine learning skill development is at the core of this programme, which is specifically designed to maximise employment potential across a wide spectrum of industrial and academic posts related to AI.
AI is rapidly changing the way we live, work and learn. Both governments and industry have recognised the need for strategic development of AI -- technology giants such as Google, Microsoft and Facebook have each established their own AI research institutes, and the UK government recently announced its £75 million investment in the November 2017 Budget.
There is however a real shortage of AI talents worldwide, both to serve the industry and drive future research. AI jobs are amongst the best paid in the industry nowadays – an AI Specialist typically earns among the highest salaries (New York Times, 22nd Oct 2017), while having a solid AI background is strongly desired in multiple research disciplines.
This MSc programme importantly recognises such need for training cutting-edge AI talents, and is specifically designed to maximise student employability on AI-specific jobs. It achieves that by putting together a programme that is:
- comprehensive, by covering all five of the most popular AI topics
- up-to-date, where each topic backed up by a world-leading group with cutting edge research
- unique, by offering Game AI that represents some of the most advanced AI to date (e.g., AlphaGo)
- practical, by focusing on developing practical machine learning skills across all five AI topics.
The programme brings together our teaching, research and industrial contacts to allow students to mix the different AI topics that best suits their personal requirements and future plans. Students will be offered lectures that explain the fundamental AI concepts, universal machine learning tools essential for any AI job profile, and specific practical and research skills on all five of the AI topics. Students will gain experience with cutting-edge tools such as Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), and Deep Reinforcement Learning (Deep RL) via regular exercises and practical labs. Students will be taught by world-renowned academics in their specific subject areas, and have regular contacts with them throughout the duration of the programme.
The industrial placement takes place from the September following the taught part of the MSc and is for a maximum of 12 months. It is a student's responsibility to secure their own placement, but the EECS Placement Team will provide support. The Placement Team source and promote suitable opportunities, assist with applications, and with interview preparation.
The industrial placement consists of 8-12 months spent working with an appropriate employer in a role that relates directly to your field of study. The placement is currently undertaken after you have completed and passed the taught component of the degree and submitted your MSc project. The placement will provide you with the opportunity to apply the key technical knowledge and skills that you have learnt in your taught modules, and will enable you to gain a better understanding of your own abilities, aptitudes, attitudes and employment potential. The module is only open to students enrolled on a programme of study with integrated placement.
In the event that you are unable to secure a placement we will transfer you onto the 1 year FT taught programme without the Industrial Experience. This change will also apply to any student visa you hold at the time.
MSc Artificial Intelligence is currently available for one-year full-time study, two years part-time study.
The programme is organised in three semesters. The first semester has four modules that operate on a 3+1 scheme: 3 core modules that cover the foundational machine learning techniques and introduction of Artificial Intelligence for Games (e.g., AlphaGo); and 1 optional module to select from three other AI topics (vision, music and language).
The second semester has four modules that are themed around all five AI topics offered. The module selection allows students to focus on topic-specific research or industry applications for AI. More importantly, these module options allow students to gain advanced and up-to-date knowledge on selected AI topics.
In the third semester, students carry out a large project on the AI topic that they want to specialise in, after agreeing on a specific topic with an academic supervisor in the first semester, and completing the preparation phase over the second semester.
Undertaking a masters programme is a serious commitment, with weekly contact hours being in addition to numerous hours of independent learning and research needed to progress at the required level. When coursework or examination deadlines are approaching independent learning hours may need to increase significantly. Please contact the course convenor for precise information on the number of contact hours per week for this programme.
Part-time study options often mean that the number of modules taken is reduced per semester, with the full modules required to complete the programme spread over two academic years. Teaching is generally done during the day and part-time students should contact the course convenor to get an idea of when these teaching hours are likely to take place. Timetables are likely to be finalised in September but you may be able to gain an expectation of what will be required.
Important note regarding Part Time Study
We regret that, due to complex timetabling constraints, we are not able to guarantee that lectures and labs for part time students will be limited to two days per week, neither do we currently support any evening classes. If you have specific enquiries about the timetabling of part time courses, please contact the MSc Administrator.
- Machine Learning (15 credits)
- Data Mining (15 credits)
- Artificial Intelligence and Games (15 credits)
- Introduction to Computer Vision (15 credits)
- Music Perception and Cognition (15 credits)
- Natural Language Processing (15 credits)
Four options from:
- Advanced Robotics Systems (15 credits)
- Music Analysis and Synthesis (15 credits)
- Information Retrieval (15 credits)
- Artificial Intelligence (15 credits)
- Music and Speech Modelling (15 credits)
- Deep Learning and Computer Vision (15 credits)
- Machine Learning for Visual Data Analysis (15 credits)
- Neural Networks and NLP (15 credits)
- Multi-platform Game Development (15 credits)
(must take and pass)
- Project (60 credits)
(must take and pass)
- Industrial Placement Project
Please note that modules are subject to change.
An upper second class degree is normally required, usually in electronic engineering, computer science, mathematics or a related discipline. Students with a good lower second class degree may be considered on an individual basis. Applicants with unrelated degrees will be considered if there is evidence of equivalent industrial experience.
For international students we require English language qualifications IELTS 6.5 or TOEFL 92 (internet based).
Learning and teaching
As a student at Queen Mary, you will play an active part in your acquisition of skills and knowledge. Teaching is by a mixture of formal lectures and small group seminars. The seminars are designed to generate informed discussion around set topics, and may involve student presentations, group exercise and role-play as well as open discussion. We take pride in the close and friendly working relationship we have with our students. You are assigned an Academic Adviser who will guide you in both academic and pastoral matters throughout your time at Queen Mary.
Teaching for all modules includes a combination of lectures, seminars and a virtual learning environment. Each module provides 36 hours of contact time, supported by lab work and directed further study.
For every hour spent in classes you will be expected to complete further hours of independent study. Your individual study time could be spent preparing for, or following up on formal study sessions; reading; producing written work; completing projects; and revising for examinations.
The direction of your individual study will be guided by the formal study sessions you attend, along with your reading lists and assignments. However, we expect you to demonstrate an active role in your own learning by reading widely and expanding your own knowledge, understanding and critical ability.
Independent study will foster in you the ability to identify your own learning needs and determine which areas you need to focus on to become proficient in your subject area. This is an important transferable skill and will help to prepare you for the transition to working life.
Modules are assessed through a combination of coursework and written examinations. You will also be assessed through an individual project.
The MSc research project will be conducted under close supervision throughout the academic year, and is evaluated by thesis, presentation and viva examination.
Our outstanding resources
- We offer our students use of their own high-specification computing and research labs, hosting over 350 state-of-the-art computers for exclusive use by our students.
- Our spectrum of research areas is supported by a range of specialist research labs offering cutting edge tools and technology including our augmented human interaction (AHI) laboratory combining pioneering technologies of full-body and multi-person motion capture, virtual and augmented reality systems and advanced aural and visual display technologies. We also have specialist laboratories in multimedia; telecommunication networks; and antenna measurement.
Have a look around by visiting our facilities pages for further information.
Tuition fees for Home and EU students2018/19 Academic Year
Thick Sandwich £9,250
Part-time study is not available for this course
Tuition fees for International students2018/19 Academic Year
Thick Sandwich £19,500
Part-time study is not available for this course
There are a number of sources of funding available for Masters students.
These include a significant package of competitive Queen Mary University of London (QMUL) bursaries and scholarships in a range of subject areas, as well as external sources of funding.
Queen Mary bursaries and scholarships
We offer a range of bursaries and scholarships for Masters students including competitive scholarships, bursaries and awards, some of which are for applicants studying specific subjects.
Find out more about QMUL bursaries and scholarships.
Alternative sources of funding
Home/EU students can apply for a range of other funding, such as Professional and Career Development Loans, and Employer Sponsorship, depending on their circumstances and the specific programme of study.
Overseas students may be eligible to apply for a range of external scholarships and we also provide information about relevant funding providers in your home country on our country web pages.
Download our Postgraduate Funding Guide for detailed information about postgraduate funding options for Home/EU students.
Tel: +44 (0)20 7882 5079
Other financial help on offer at Queen Mary
We offer one to one specialist support on all financial and welfare issues through our Advice and Counselling Service, which you can access as soon as you have applied for a place at Queen Mary.
Our Advice and Counselling Service also has lots of Student Advice Guides on all aspects of finance including:
Tel: +44 (0)20 7882 8717